DS402
Python for Data Scientists
Faculty
Maxim Musin
CEO at rebels.ai
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
The course will cover basic python methods for data analysis: pandas, numpy, scipy, sklearn, along with advanced techniques of their application. Basic integrations of python with external libraries like xgboost, tensorflow, pytorch along with data wrangling and some hyperparameter optimization methods will be also included. Jupyter notebook usage and tricks will be also given as an organic part of the course. At the end of module, everyone is expected to be ready to come up with a simple data wrangling system.
Learning highlights
- Working with the basic package: jupyter, pandas, numpy, scipy in more details, so students will not have problems in the future with data wrangling, particularly with merging several data sources in one. For sklearn we will consider custom modification for all the pipeline steps. Students will be introduced to the usual problems of a python environment setup for data analysis, and they will receive a basic experience of xgboost, tensorflow, pytorch. Students will also be shown examples of useful system applications, like automl and hyperparameter optimization.
Course outline
15 classes
Class 1
Jupyter notebooks, tricks, hotkeys. Python methods integrated with jupyter
Class 2
Data manipulation. Pandas. Reading .csv files, Titanic dataset. Manipulating the dataset in a number of ways.
Class 3
Data visualization. Matplotlib, seaborn, bokeh (advanced level).
Class 4
Sklearn. Basic ML concepts: cross validation, fit/predict. Preparing prediction for Titanic dataset
Class 5
Checking homework assigments on data manipulation and visualization. Sklearn and numpy methods.
Class 6
Data versioning. Working with enterprise data analysis systems, pitfalls and techniques.
Class 7
Weekly homework revisiting. Performing data analysis at the scale.
Class 8
Storing custom approximators as custom sklearn classes. Sklearn pipelines.
Class 9
Feature engineering. Basic textual features and image data extraction.
Class 10
Advanced basic approximators to use in practice: xgboost, vw. Of the shelf hyperparameter optimization. Automl.
Class 11
Python and jupyter integrations. Google docs, chatbots, interface prototyping, data annotation, scrapping
Class 12
Heavy dataset processing with python instruments.
Class 13
Consultation on student projects.
Class 14
Finals
Class 15
Student project demonstration.
Methodology
We will study a set of practical jupyter notebooks, interrupted with relatively short theoretical parts. There will be 2 big homework assignments designed to emulate a relatively real data science project. There will also be personal projects based on python integrations and capabilities of data analysis - this will be a good example of time management in a DS project. Also, there will be a final exam and student project demonstration at the end of the course.
Grading
Maxim Musin comes from a background in statistics, advanced multidimensional probability, and random processes. During his career in these fields, he found himself developing skills and gathering experience through working in both academic environments and the private sector. For the last 5 years Maxim is a CEO of for profit AI development laboratory rebels.ai, integrating AI in enterprise and helping startups reach the orbit.
His academic experience ranges from teaching probability and statistics at MSU and MIPT, as a member of the faculty of innovation and high technology, FIHT, which at the time was among the few places worldwide with capabilities for advanced statistics study. During his time there, he produced several notable projects with his students, particularly in regards to the stochastic convergence of neural networks. His course on applied modern statistics became mandatory for the data analysis division of the FIHT MIPT Masters.
See full profileApply for this course
Python for Data Scientists
by Maxim Musin
Total hours
45 Hours
Dates
Oct 19 - Nov 06, 2020
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
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Schedule your Harbour.Space interview
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FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.